Atmosphere (대기)

Korean Meteorological Society (한국기상학회)
권호 표지
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MTSAT Satellite Image Features on the Sever Storm Events in Yeongdong Region

영동지역 악기상 사례에 대한 MTSAT 위성 영상의 특징

  • Kim, In-Hye (Korea Rural Economic Institute) ;
  • Kwon, Tae-Yong (Department of Atmospheric and Environmental Sciences, Gangneung-Wonju National University) ;
  • Kim, Deok-Rae (Climate Change Research Division, Climate and Air Quality Research Department, National Institute of Environmental Research)
  • 김인혜 (한국농촌경제연구원) ;
  • 권태영 (강릉원주대학교 대기환경과학과) ;
  • 김덕래 (국립환경과학원 기후대기연구부 기후변화연구과)
  • Received : 2011.10.26
  • Accepted : 2012.01.16
  • Published : 2012.03.31
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Abstract

An unusual autumn storm developed rapidly in the western part of the East sea on the early morning of 23 October 2006. This storm produced a record-breaking heavy rain and strong wind in the northern and middle part of the Yeong-dong region; 24-h rainfall of 304 mm over Gangneung and wind speed exceeding 63.7 m $s^{-1}$ over Sokcho. In this study, MTSAT-1R (Multi-fuctional Transport Satellite) water vapor and infrared channel imagery are examined to find out some features which are dynamically associated with the development of the storm. These features may be the precursor signals of the rapidly developing storm and can be employed for very short range forecast and nowcasting of severe storm. The satellite features are summarized: 1) MTSAT-1R Water Vapor imagery exhibited that distinct dark region develops over the Yellow sea at about 12 hours before the occurrence of maximum rainfall about 1100 KST on 23 October 2006. After then, it changes gradually into dry intrusion. This dark region in the water vapor image is closely related with the positive anomaly in 500 hPa Potential Vorticity field. 2) In the Infrared imagery, low stratus (brightness temperature: $0{\sim}5^{\circ}C$) develops from near Bo-Hai bay and Shanfung peninsula and then dissipates partially on the western coast of Korean peninsula. These features are found at 10~12 hours before the maximum rainfall occurrence, which are associated with the cold and warm advection in the lower troposphere. 3) The IR imagery reveals that two convective cloud cells (brightness temperature below $-50^{\circ}C$) merge each other and after merging it grows up rapidly over the western part of East sea at about 5 hours before the maximum rainfall occurrence. These features remind that there must be the upward flow in the upper troposphere and the low-layer convergence over the same region of East sea. The time of maximum growth of the convective cloud agrees well with the time of the maximum rainfall.

Keywords

References

  1. 권태영, 2001: GMS-5 IR1 밝기온도와 AWS 강우량의 관계성; 1998년 8월중서부지역집중호우사례. 대한원격탐사학회지, 17(1), 15-31.
  2. 오재호, 홍성길, 1995: 대기 중 $CO_2$ 증가에 따른 한반도 강수량 변화. 한국수자원학회지, 28(3), 143-157.
  3. 조구희, 2008: 개념 모델을 통한 영동지역의 겨울철 지형성 강수에 대한 통계적 예보. 강릉대학교 대학원 박사학위논문, 143 pp.
  4. Adler, R. F., and D. D. Fenn, 1981: Satellite-observed cloudtop height changes in tornadic thunderstorms. J. Appl. Meteor., 20, 1369-1375. https://doi.org/10.1175/1520-0450(1981)020<1369:SOCTHC>2.0.CO;2
  5. Adler, R. F., and A. J. Negri, 1987: A satellite infrared technique to estimate tropical convective and stratiform rainfall. J. Appl. Meteor., 27, 30-51.
  6. Barrett, E. C., and D. W. Martin, 1981: The use of satellite data in rainfall monitoring. Academic Press, 340 pp.
  7. Baum, B. A., V. Tovinkere, J. Titlow, and R. M. Welch, 1997: Automated cloud classification of global AVHRR data using a fuzzy logic approach. J. Appl. Meteor., 36, 1519-1540. https://doi.org/10.1175/1520-0450(1997)036<1519:ACCOGA>2.0.CO;2
  8. Bellerby, T. J., and J. Sun, 2005: Probabilistic and Ensemble Representations of the Uncertainty in an IR/Microwave Satellite Precipitation Product. J. Hydrol., 6, 1032-1044.
  9. Delgado, G., L. A. T. Machado, C. F. Angelis, M. J. Bottino, A. Redao, J. Lorente, L. Gimeno, and R. Nieto, 2007: Basis for a rainfall estimation technique using IR-VIS cloud classification and parameters over the life cycle of mesoscale convective systems. J. Appl. Meteor. Climal., 47, 1500-1517.
  10. Demirtas, M., and A. J. Thorpe, 1999: Sensitivity of shortrange weather forecasts to local potential vorticity modifications. Mon. Wea. Rev., 127, 922-939. https://doi.org/10.1175/1520-0493(1999)127<0922:SOSRWF>2.0.CO;2
  11. Georgiev, C. G., 1999: Quantitative relationship between meteosat WV data and positive potential vorticity anomalies: a case study over the mediterranean. Meteorol. Appl., 6, 97-109. https://doi.org/10.1017/S1350482799001024
  12. Gordon, H. B., P. H. Whetton, A. B. Pittok, A. M. Fowler and M. R. Haylock, 1992: Simulated changes in daily rainfall intensity due to the enhanced greenhouse effect: implications for extreme rainfall events. Climate Dyn., 8, 83-102. https://doi.org/10.1007/BF00209165
  13. Griffith, C. G., W. L. Woodley, P. G. Grube, D. W. Martin, J. stout, and D. N. Sikdar, 1978: Rain estimation from geosynchronous satellite imagery-visible and infrared studies. Mon. Wea. Rev., 106, 1153-1171. https://doi.org/10.1175/1520-0493(1978)106<1153:REFGSI>2.0.CO;2
  14. Hanley, D. E., 2002: The evolution of a hurricane-trough interaction from a satellite perspective. Wea. Forecasting, 17, 916-926. https://doi.org/10.1175/1520-0434(2002)017<0916:TEOAHT>2.0.CO;2
  15. Hong, Y., 2004: Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. J. Appl. Meteor., 43, 1834-1852. https://doi.org/10.1175/JAM2173.1
  16. Hoskins, B. J., M. E. Mclntyre, and A. W. Robertson, 1985: On the use and significance of isentropic potential vorticity maps. Q. J. R. Meteorol. Soc., 111, 877-946. https://doi.org/10.1002/qj.49711147002
  17. Huo, Z., D.-L. Zhang, and J. Gyakum, 1998: An application of potential vorticity inversion to improving the numerical prediction of the march 1993 superstorm. Mon. Wea. Rev., 126, 424-436. https://doi.org/10.1175/1520-0493(1998)126<0424:AAOPVI>2.0.CO;2
  18. Kidd, C., D. R. Kniveton, M. C. Todd, and T. J. Bellerby, 2003: Satellite rainfall estimation using a combined passive microwave and infrared algorithm. J. Hydrol., 4, 1088-1104.
  19. Kuligowski, R. J., 2002: A self-calibrating real-time GOES rainfall algorithm for short-term rainfall estimates. J. Hydrol., 3, 112-130.
  20. Kurz, M., 1994: The role of diagnostic tools in modern weather forecasting. Meteorol Appl., 1, 45-67.
  21. Levizzani, V., 1999: Convective rain from a satellite prospect: Achievements and challenges. SAF Training Workshop-Nowcasting and Very Short Forecasting. Madrid, 9-11 Dec., EUM P. 25, 75-84.
  22. Mansfield, D. A. 1996: the use of potential vorticity as an operational forecast tool. Meteorogl. Appl., 3, 195-210.
  23. Mecikalski, J. R., and K. M. Bedka, 2006: Forecasting convective initiation by monitoring the evolution of moving cumulus in daytime GOES imagery. Mon. Wea. Rev., 134, 49-78. https://doi.org/10.1175/MWR3062.1
  24. Pankiewicz, G. S., S. J. Swarbrick, and S. C. Watkin, 1999: Automatic estimation of potential vorticity from Meteosat water vapour imagery to adjust initial fields in NWP. In The 1999 Meteorological Satellite Data Users' Conference, EUM P, 26, ISSN 1011-3932, EUMETSAT, Lighthous Multimedia Darmstadt, 387-394.
  25. Roberts, N. M., and H. W. Lean, 2008: Scale-selective verification of rainfall accumulations from highresolution forecasts of convective events. Mon. Wea. Rev., 136, 78-97. https://doi.org/10.1175/2007MWR2123.1
  26. Scofield, R. A., 1987: The NESDIS operational convective precipitation estimation technique. Mon. Wea. Rev., 115, 1773-1792. https://doi.org/10.1175/1520-0493(1987)115<1773:TNOCPE>2.0.CO;2
  27. Swarbrick, S. J. 2001: Applying the relationship between potential vorticity fields and water vapour imagery to adjust initial conditions in NWP. Meteorol. Appl., 8. 221-228. https://doi.org/10.1017/S1350482701002109
  28. Turk, F. J., E. F. Ebert, H.-J. Oh, B.-J. Shon, V. Levizzani, E. A. Smith and R. R. Ferraro, 2003: Validation of an operational global precipitation analysis at short time scales. Prepr. 12th Conf. on Satellite Meteor. and Oceanography, Long Beach, CA, 9-13 Feb., paper 1.2, 21.
  29. Uccellini, L. W., D. Keyser, K. F. Brill, and C. H. Wash, 1985: The presidents' day cyclone of 18-19 February 1979: Influence of upstream trough amplification and associated tropopause folding on rapid cyclogenesis. Mon. Wea. Rev., 113, 962-988. https://doi.org/10.1175/1520-0493(1985)113<0962:TPDCOF>2.0.CO;2
  30. Uddstrom, M. J., and W. R. Gray, 1996: Satellite cloud classification and rain-rate estimation using multispectral radiances and measures of spatial texture. J. Appl. Meteor., 35, 839-858. https://doi.org/10.1175/1520-0450(1996)035<0839:SCCARR>2.0.CO;2
  31. Vincente, G. A., R. A. Scofield, and W. P. Menzel, 1998: The operational GOES infrared rainfall estimation technique. Bull. Amer. Meteor. Soc., 79, 1883-1898. https://doi.org/10.1175/1520-0477(1998)079<1883:TOGIRE>2.0.CO;2
  32. Weckwerth, T. M., D. B. Parsons, S. E. Koch, J. A. Moore, M. A. Lemone, B. B. Demoz, C. Flamant, B. Geerts, J. Wang, and W. F. Feltz, 2004: An overview of the International $H_2O$ Project (IHOP_2002) and some preliminary highlights. Bull. Amer. Meteor. Soc., 85, 253-277. https://doi.org/10.1175/BAMS-85-2-253
  33. Weldon, R. B., and S. J. Holmes, 1991: Water vapor imagery: interpretation and applications to weather analysis and forecasting. NOAA Tec. Rep. NESDIS 57, Washington D.C, 213 pp.
  34. Weng, F., L. Zhao, R. R. Ferraro, G. Poe, X. Li, and N. C. Grody, 2003: Advanced Microwave Sounding Unit cloud and precipitation algorithms. Radio Sci., 38, 8068-8079.
  35. Young, M. V., G. A. Monk, and K. A. Browning, 1987: Interpretation of satellite imagery of a deepening cyclone. Q. J. R. Meteorol. Soc., 113, 1089-1115.

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